ASHS 2025 Conference

Florida Medallion™ ‘FL 16.30-128’ strawberry (Fragaria x ananassa; hereafter referred to as Medallion; U.S. Patent PP33,451) was released from the University of Florida in 2020. By 2024 this cultivar occupied approximately 15% of acreage in central Florida. The unique characteristics of this cultivar, in particular its early yields and fruity flavor, point to the breeding strategy employed in its development. Medallion originated from a 2016 cross between two unreleased selections. Marker-assisted selection was applied to this cross using a PCR-based marker for the FaFAD1 gene controlling production of gamma-decalactone, a volatile imparting fruity flavor. Seedlings homozygous for the functional allele were retained. At the advanced selection stage, trials of Medallion were conducted at the research plots of the University of Florida Gulf Coast Research and Education Center (GCREC) in Wimauma, FL and fruit were harvested for sensory and chemical analyses over five seasons. The increased dosage of the FaFAD1 gene resulted in high production of gamma-decalactone. Trained sensory panels perceived improved sweetness and strawberry flavor intensity of Medallion to be above the current industry standard ‘Florida Brilliance’. Additional background and data will be presented illustrating the early yield of this variety and other characteristics making it suitable for the central Florida industry and suggesting future breeding strategies for improving strawberry flavor.

Strawberries rank among the most economically significant horticultural crops in the United States, with a production value of approximately $3.4 billion in 2023. Year-round demand and widespread popularity necessitate extensive efforts to improve fruit quality, yield, and production in controlled environment agriculture (CEA). As part of these initiatives, optimizing runner production for the year-round availability of planting materials is crucial. The importance of photoperiod and light intensity in runner production has been highlighted previously; however, a thorough exploration of the relationship between the total light quantity received during the entire experimental period and runner production is lacking. This study assessed the responses of strawberry genotypes to sunlight and high-performance LED lighting in runner production. Continuous measurements of photosynthetic photon flux density (PPFD) and daily light integral (DLI) provided an accurate assessment of light exposure during the cultivation period across eight strawberry accessions. In a glasshouse under sunlight, plants experienced variable light conditions due to fluctuating weather, with an average DLI of approximately 10 mol m⁻² d⁻¹, whereas under LED lighting in a growth chamber, a stable DLI of 44.1 mol m⁻² d⁻¹ was recorded. No runner formation was observed under sunlight over 44 days, whereas minimal production in two accessions, PI 551445 and PI 616509, was observed within 12 days following the previous 44-day experimental period, with each accession producing one runner. In contrast, stable and high DLI led to significantly higher runner production. When an ANOVA test was conducted using only runner count data from the stable and high DLI conditions, significant differences in runner formation were observed among the tested strawberry accessions (F value = 2.91, p = 0.03). Accessions PI 616509 and PI 679822 produced the most runners, averaging 6.5 and 4.5, respectively, whereas PI 551692 and PI 551445 produced none and one runner, respectively. These results suggest that strawberry runner production depends on cumulative light exposure and genetic makeup. Overall, these findings provide valuable insights into optimizing strawberry runner production in CEA, demonstrating that stable, high-intensity LED lighting can effectively overcome the limitations of variable natural light and enhance year-round production efficiency.

Plant breeding is a lengthy and demanding research. Traditional strawberry breeding requires many man-hours to manually measure plant characteristics, record data, and evaluate various desired traits. Also, human biases and prior perceptions or expectations can play a role in skewing the results. Thus, the plant breeding program at the University of Florida has developed AI tools to assist in different stages of breeding research. Developed AI models have offered accurate and quick data analysis to identify and quantify plant phenome (anatomical characteristics and traits). This reduction in the number of manhours to manually measure, record data, and perform destructive sampling, has greatly increased the ability to screen more breeding lines with fewer resources (time, plants, and money). These AI models can accurately with a high level of consistency measure the size of plant canopy, flowers, runners, and fruit maturity repeatedly throughout the season to create an individual profile of each tested breeding line. Five YOLOv8 based (computer vision) models were trained for strawberry runner detection including GI, UL-AI, SL-AI, Hybrid I (GI SL-AI), and Hybrid II (GI SL-AI UL-AI). Hybrid II model achieved 91% precision accuracy and 83% mAP50 (mean average precision at IoU of 50%). The use of AI image and video analysis has been reducing the time and resources needed to develop new varieties.

Powdery mildew (PM) is a significant fungal disease in controlled environment horticulture (CEH). PM damages are increasingly being reported in fungicide-untreated and late planted open fields. Both field and protected systems in the major production regions of the world are facing heightened challenges due to the increasing disease occurrences. While PM has been efficiently managed by spraying chemical fungicides in field production, frequent use increases the risk of resistance development in pathogens. Additionally, fungicidal sprays may not be feasible to all CEH farms due to infrastructural restrictions or organic mode of production. The expansion of CEH production in Texas has created opportunities to extend strawberry cultivation from coastal regions to inland areas. However, PM management needs to be addressed given the conducive growing conditions in CEH. To study the prevalence of the disease, we evaluated 24 and 12 strawberry accessions arranged in a completely randomized design in two replications in the greenhouse and growth room conditions, respectively. In the absence of fungicide treatments, natural infections led to PM development within two weeks after transplanting. Disease ratings revealed incidence rates of 72% in the growth chamber and 49% in the greenhouse. The correlation between the two replications was 0.88 and 0.87, respectively. Accessions that exhibited little to no PM symptoms may be valuable for understanding host resistance mechanisms and could be utilized in breeding resistant cultivars in the future.

Fresh market tomato is one of the most valuable crops in the US. However, production relies heavily on manual labor, which can account for over 30% of the total per-acre cost, with a large portion attributed to harvesting. In the southeast US, most tomato plants are staked and tied, and fruit are hand-harvested multiple times as they mature, increasing labor costs and operational inefficiencies. Compact Growth Habit (CGH) tomato varieties have a shorter stature that does not need to be staked and allow for more labor-efficient harvesting options, providing a promising alternative to traditional production. A key breeding objective for CGH tomato is to develop lines with a more concentrated fruit set (CFS), defined as a higher proportion of fruits reaching maturity synchronously. This trait would enable once-over harvesting, substantially reducing labor inputs while improving operational efficiency. Furthermore, the successful implementation of once-over harvest strategies in CGH tomatoes may facilitate the adoption of mechanized harvesting systems, addressing labor shortages and enhancing scalability in fresh market tomato production. This study aims to develop a computer vision model to automate detecting and quantifying tomato fruits and flowers in CGH breeding trials. High-resolution RGB images of top-view canopies were collected from experimental plots during the spring and fall seasons of 2024, capturing phenotypic variability across diverse environmental conditions and growth stages. The dataset is undergoing preprocessing, annotation, and augmentation to enhance model robustness. A YOLO-based object detection model will be trained to classify and quantify flowers and fruits. Model performance will be assessed using standard evaluation metrics, including precision, recall, and F1-score. By accurately detecting and quantifying fruits and flowers across developmental stages, this system will enable breeders to analyze flowering progression and identify CGH tomato lines with improved CFS, supporting the selection of varieties optimized for once-over harvesting. Preliminary model training using 1,370 training images, 116 validation images, and 335 test images in roboflow using YOLOv11 yielded promising results, with a mAP@50 of 94.7%, precision of 85.1%, and recall of 91.0%, demonstrating the model's potential to support phenotyping for concentrated fruit set. Future research will focus on enhancing detection accuracy, expanding dataset diversity, and integrating multispectral imaging techniques to optimize model performance and applicability across different environments.

Papaya ringspot disease caused by papaya ringspot virus P (PRSV-P), is restricting the commercial cultivation of papaya worldwide. Several measures have been taken to control the disease, including the application of aphicides, identifying host plant resistance, and transgenics. However, only genetically engineered papayas carrying the viral coat protein have been found to effectively control the disease. Transgenic papayas are not cultivated worldwide due to ethical regulations. Assessing the diverse papaya germplasm for resistance to PRSV could be a suitable alternative. Therefore, the present study was undertaken to assess PRSV resistance in 96 accessions, including 36 commercial accessions and 57 wild accessions. To identify novel resistant sources, the accessions were mechanically inoculated with PRSV under controlled conditions. The inoculated plants were continuously monitored for the appearance of PRSV-like symptoms and scored for disease severity, ranging from 0-5. Among all the accessions tested, only two accessions, HCAR 46 (Vasconcellea pubescens) and HCAR 177 (V. stipulate), did not show any symptoms. To further assure that no virus is present in these plants, an RT-qPCR was performed with PRSV coat protein-specific primers. The accession HCAR 46 showed the presence of a faint amplicon of 950 bp. However, no PSRV-specific amplicon was observed in HCAR 177. To further confirm the presence of PRSV, the amplified products were sequenced and showed over 95% sequence similarity with PRSV. The PRSV-resistant genotype identified in the present study could be used to breed PRSV-resistant cultivars.

Verifying Parentage of Offspring from Crosses of the Pawpaw (Asimina triloba) Cultivar ‘Sunflower’ and the Cultivars ‘Susquehanna’ and ‘KSU-Chappell’ using Simple Sequence Repeat (SSR) Markers Nabin K. Adhikari, Dr. Kirk W. Pomper, Jeremy Lowe, Dr. Srijana Thapa Magar, and Sheri Crabtree College of Agriculture, Health and Natural Resources, Kentucky State University Pawpaw (Asimina triloba), a North American tree fruit in the early stages of commercialization, is typically found in clonal patches in forest understories. Cultivated pawpaws exhibit superior size, flavor, and appearance compared to their wild counterparts. Pawpaw is generally considered self-incompatible due to its protogynous flowers, though anecdotal evidence suggests self-compatibility in the ‘Sunflower’ variety. Previous research at Kentucky State University used simple sequence repeat (SSR) markers, a class of co-dominant genetic markers that target hypervariable regions of the genome, to confirm that ‘Sunflower’ is capable of self-pollination. However, there is little information on optimal pollinizer relationships and which cultivars serve to promote fruit set in cross-pollinations. This study aims to verify the parentage of offspring from crosses between ‘Sunflower’ and the cultivars ‘Susquehanna’ and ‘KSU-Chappell’, as well as the advanced selection 7-90 using simple sequence repeat markers. Leaves of parent trees and offspring were collected for DNA extraction. Young leaves were collected and were frozen (-15 ºC) until DNA extraction. DNA extraction was carried out using a DNAMITE Plant Kit (Microzone Ltd. Haywards Heath, West Sussex, UK). PCR was performed using SSR markers developed by Pomper et al., 2010. PCR products will be separated using a SeqStudio (Applied Biosystems, Foster City, CA) capillary electrophoresis system, and genotyping will be performed with GeneMapper software (Applied Biosystems, Foster City, CA). Parentage verification will be determined by looking for the presence or absence of alleles from the purported parents in the offspring. Offspring were categorized as self-pollinated if only alleles from the pollen recipient parent were present in the offspring, expected cross-pollinated if alleles from both parents were present, or unexpected if unknown alleles were detected.

The need to improve crops has never been critical with the rising population and climate change resulting in high abiotic stress and disease pressures in production areas. In recent years, artificial intelligence (AI)-based approaches have been implemented in the context of plant breeding and crop improvement. Modern AI tools hold the promise of accelerating the development of resilient, higher-yielding, and more sustainable horticultural crops, by rendering a deeper understanding of complex genetic systems and phenotypes, and how these interact with their environment to express desirable traits. As an approach, AI is an important component of the plant breeding toolbox which may now currently be an indispensable addition to modern vegetable breeding programs. For example, AI allows for the prediction of phenotypic values through genetic markers, and this allows plant breeders to perform selection even before the trials are conducted in the field. The ASHS Vegetable Breeding and Interest Group seeks to provide research updates from experts who have worked on the applications of AI in crop breeding and genetic improvement. The workshop will provide a summary of various AI methodologies, recent advances, and render opportunities for future collaboration and research directions in the implementation of AI in vegetable breeding programs. Objectives 1. Summarize the different AI approaches used in breeding and genetic improvement of various traits in vegetables 2. Provide the attendees with recent advances in AI for plant breeding 3. Discuss future research directions and applications of AI in plant breeding programs The workshop will be conducted during the annual ASHS meeting (July 28- August 1, 2025) in New Orleans, Louisiana. The workshop will be in-person. Audience: The workshop will be open to all ASHS attendees (both public and private sectors) and will be interactive.

Moderators: Dennis Lozada, New Mexico State University
Devi Kandel, Langston University

Speakers:

• Cheryl Dalid, University of Florida - Leveraging Phenomics and Genomics Data in Strawberry Breeding
• Stephen Ficklin, Washington State University - Towards Identification of Biomarkers for Environmentally-controlled Traits
• Madhi Haghshenas-Jaryani, New Mexico State University - AI-enabled Agricultural Robots and Intelligent Machines for Precision Farming of Chile Pepper Cultivation in New Mexico
• Tanzeel Rehman, Auburn University - AI-Driven High-Throughput Phenotyping for Assessing Physiological Stress in Blueberry
• Kevin Wang, University of Florida - AI-Powered Phenomics: Accelerating Breeding Across Horticultural Crops